The present invention relates to methods and systems for presenting multi-dimensional data in an integrated computing environment and, in particular, to methods, systems, and devices for dynamically determining and presenting extrapolations of multi-dimensional data related to manufacturing processes.
Computer-implemented integrated production management systems are increasingly being used in manufacturing enterprises. Such systems, often referred to as “supply chain management”, model the enterprise environment and provide for planning of producing items to fulfill market demand within the constraints of the environment.
In a simplest sense, the supply chain is a process of creating products for consumption. Generally, supply chains span from raw materials to manufacturing, distribution, transportation, warehousing, and product sales, involving a number of enterprises before a product reaches a customer. Thus, a data management system for a single manufacturing enterprise within a supply chain typically includes interactions with preceding and subsequent stages of the same chain in order to provide for an accurate representation of the manufacturing process.
Manufacturing enterprises generally rely on a variety of tools and procedures to control and analyze production and business operations, such as material and resource inventories, production capacities, production and maintenance scheduling, process/production unit (machine, work cell, process line) performance monitoring on one hand and accounting, payroll, human resources, employee tracking, customer relations tracking on the other. These procedures reflect different functions, aspects or “dimensions” of the business operation and, accordingly, often rely on multi-dimensional data.
Tools which provide these functions are typically implemented using computer software. For example, one software package may manage business accounting, another software package might be responsible for receiving and processing new orders, yet another software package will track warehouse inventory and still other packages may handle order fulfillment and shipment. Furthermore, a business software package operated by one business may need to exchange data with a software package operated by another business to facilitate a business-to-business transaction. Integration applications have been developed which are used to integrate various elements of one business application with elements of another business application.
One of many consequences of the increasing computerization of production and business operations is generation of vast quantities of complex multi-dimensional data. While capturing multiple aspects of the business activities is becoming easier, comprehending data so acquired is increasingly challenging. Because of the complexity of the supply chain management, it is important to present information to the user in a comprehensible manner. Good presentations are vital to the usefulness of the supply chain management tools.
Abstract data used in business and technical pursuits commonly consists of categories, rankings, and real value measurements gathered by people and machines. A number of conventional techniques for organizing, summarizing, and presenting such data, for example, tables, statistics, and graphics, have been developed. Conventional techniques have also been developed for organizing the storage and retrieval of such data such as hierarchical, relational, and object-oriented databases as well as non-database methods such as “flat” files, spreadsheets, or other data structures. Collecting, storing and accessing data, however, is only the beginning of process of turning raw (abstract) data into valuable information.
Database management systems provide basic database operations, such as storage and retrieval of records based upon selection criteria or filters. Analytical software enables other more advanced types of database operations. Basic operations include entering, updating, deleting and retrieving sets of data from the database. More advanced operations include creating new attributes by transforming original attributes or aggregating sets of attributes or records.
Comprehending data by the user involves reducing the data to a series of visualizations or other kinds of mental representations, often fitting these representations into a pre-existing model. The model abstracting the data enables the user to make decisions and take or avoid actions based on the model and the user's projection of its consequences. Such approach necessarily simplifies the data. The same data may support several different models based on differing presuppositions.
The major activities of a data analyst/user, common in managing of any business, may be characterized by model fitting or data exploration. In model fitting, a predefined model exists and the data is used to calculate or pick the parameters of the model, for example, to predict outcomes. In data exploration, visual methods are often used to summarize the data with a goal of identifying an appropriate model. In practice, especially for highly dimensional data, both activities are performed iteratively with models being selected and fit and then discarded for a new model as understanding of the data set improves. The pace at which this process can proceed is limited by the availability of visualization tools that aid the data analyst in perceiving the data.
A combination of database operations and graphical display techniques is used to build an intuitive “feel” for data, examine how well putative models perform, identify database errors, and examine relationships among data subsets. Graphical representation tools are very useful in maintaining and analyzing data. Most graphic user interfaces allow applications to establish dynamic data links for exchanging data between applications. Typically, these links allow changes in one application to be immediately reflected in other applications.
Even using the combination of complex database operations and graphical display techniques, it is difficult to gain an understanding of highly distributed multi-dimensional data because there are simply too many values to mentally track or plot. Thus, these types of data are typically reduced using data transformations to a form that can be displayed with conventional graphical methods. For example, transforming data by aggregating across variables reduces data dimensionality by creating new variables that summarize several original variables. As a specific example, sales and expenses may be recorded in a database, whereas the difference of the two representing a profit or loss may be more meaningful in a business model and can serve to reduce the amount of data being viewed.
Such user interfaces are typically custom solutions which must be specifically coded for each integration application. Moreover, the multi-dimensional data need to be displayed in meaningful ways, so the users that differ in fields of expertise, duties, capacities, and comprehension could interact within the system and communicate successfully with each other to attain common goals. In existing systems, there are a number of graphical formats used to display data two or more attributes at a time. In these formats time or another index attribute is displayed as one coordinate in a static display. The exponential growth of the computer processing power available for data modeling and graphical data exploration is used by current tools merely to display larger data sets faster. Thus, known data visualization techniques continue to present a number of limitations.
Therefore it would be desirable to provide a user-friendly data management system for manufacturing enterprise capable of visually representing projected results of current activities in a manner that is comprehensible to a number of users.